Our ability to annotate and identify proteins and pathways that may be important targets to treat diseases is limited by the lack of systematic understanding of how proteins and functional modules evolved to carry out desired functions. While genomic projects accumulated vast amount of data on sequence, structure and function of proteins its systematic analysis is not possible in the absence of theory that relates their molecular properties to the functional constraints and evolutionary requirement on organisms that carry the genomes. This proposal aims to develop such theory where molecular evolution of proteins is studied in the context of Darwinian evolution of organisms that carry them. Theoretical and experimental research proposed here aims to address the following questions: 1) How did modern Universe of protein structures evolve in early biological evolution under the environmental and competitive constraints on organisms? Why some protein folds are overrepresented in many proteins and some are unique? 2) How do organisms adapt to extreme environmental conditions and how is that adaptation manifest in the compositional and structural repertoire of their genomes and proteomes? 3) How did new protein functions, such as error correction evolve in the process of conversion from RNA to DNA world? 4) How did participants in biological networks - transcription factors and upstream regions - co-evolve and how is it reflected in their phylogenetic profiles? 5) How did protein-protein interactions responsible for immune response evolve? 6) How does fitness landscape of an organism depend on molecular properties (such as stability) of proteins constituting it? These questions will be addressed using multi-tool approach that includes analytical theory, simulations of detailed microscopic evolutionary models using coarse grained and realistic representations of protein structures and experimental research that involves newly developed competitive fluorescent assays for wild type and mutant variant of E.coli as a model system.
This theoretical and experimental study aims to discover how protein structures and functions evolve in response to functional demands of organisms. It will help to identify biological modules that are responsible for diseases such as autoimmunity and immune deficiency and will help to formulate better anti-viral strategies.
|Gilson, Amy I; Marshall-Christensen, Ahmee; Choi, Jeong-Mo et al. (2017) The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution. Biophys J 112:1350-1365|
|Bershtein, Shimon; Serohijos, Adrian Wr; Shakhnovich, Eugene I (2017) Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations. Curr Opin Struct Biol 42:31-40|
|Choi, Jeong-Mo; Gilson, Amy I; Shakhnovich, Eugene I (2017) Graph's Topology and Free Energy of a Spin Model on the Graph. Phys Rev Lett 118:088302|
|Adkar, Bharat V; Manhart, Michael; Bhattacharyya, Sanchari et al. (2017) Optimization of lag phase shapes the evolution of a bacterial enzyme. Nat Ecol Evol 1:149|
|Jacobs, William M; Shakhnovich, Eugene I (2016) Structure-Based Prediction of Protein-Folding Transition Paths. Biophys J 111:925-36|
|Jacquin, Hugo; Gilson, Amy; Shakhnovich, Eugene et al. (2016) Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models. PLoS Comput Biol 12:e1004889|
|Rodrigues, João V; Bershtein, Shimon; Li, Anna et al. (2016) Biophysical principles predict fitness landscapes of drug resistance. Proc Natl Acad Sci U S A 113:E1470-8|
|Bershtein, Shimon; Choi, Jeong-Mo; Bhattacharyya, Sanchari et al. (2015) Systems-level response to point mutations in a core metabolic enzyme modulates genotype-phenotype relationship. Cell Rep 11:645-56|
|Çetinba?, Murat; Shakhnovich, Eugene I (2015) Is catalytic activity of chaperones a selectable trait for the emergence of heat shock response? Biophys J 108:438-48|
|Zhou, Qingtong; Xia, Xiaole; Luo, Zhaofeng et al. (2015) Searching the Sequence Space for Potent Aptamers Using SELEX in Silico. J Chem Theory Comput 11:5939-46|
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